Information Technology AND Healthcare? Why on Earth would you combine such incompatible career fields?

I can’t tell you how many times I was questioned about this in my past. Early on in my career, no one ever told me that my early pursuits of combining my Computer Operations training in the Air Force with my decision to pursue medicine was actually a good idea. In fact, it was quite the opposite. And yet - this year I can give about 45,000 more reasons (the number of attendees at HIMSS 2017 [1]) on why the path led to a promising merging career field after all.

The “missing link” career - people divided by a common career field.

Risk stratification has, so far, been biased toward structured data due to accessibility issues. As interest in long-term member wellness increases in importance it is the insights trapped in unstructured data that will become the differentiator in a changing and competitive market. The payers who are able to characterize member groups at a fundamentally more detailed level will have the advantage of population insight over those who struggle to do so.

Data sources that are increasing in scale and availability include electronic healthcare records (EHRs) data in Continuity of Care Document (CCD) format from providers, OCR notes about members, and nurses’ notes.

How can payers make effective use of unstructured data to stratify populations more effectively when much of their infrastructure is tied to structured data? Sources of unstructured data contain significantly more detail about members but are much more varied.

Here at Linguamatics Health, our Clinical NLP specialists understand the urgency and complexity of bringing together data sources, both structured and unstructured, in a workflow that gets you to insights you need quickly.

Ever find an acute problem such as a fracture, which shows in a Problem List, but healed months ago? Or perhaps the problem list states a case of bronchitis that may have been transient or may actually be Chronic Obstructive Pulmonary Disease (COPD)? After all, a diagnosis of COPD is a collaboration of symptoms and test results. How many clinicians find the spare time to go retrospectively back in the EHR and calculate a patient’s, “coughing with excessive sputum nearly everyday for at least 3 months of the year, for 2 years in a row” [1]?

But fixing the problem list can be time-consuming and complicated. Isn’t there an alternative (better) way?

Many organizations believe that in order to derive an accurate picture of their population’s health, medication lists can be just as good as their problem list. What if you find a patient taking an atypical antipsychotic medication and they don’t have a diagnosis that coincides on their Problem List? Can we just assume a mental health diagnosis? After all, this conclusion seems logical. Or is it? Is it an oversight on their Problem List or are they prescribed it for an off-label reason? According to the Agency for Healthcare Research and Quality (AHRQ), a 2011 report stated off-label atypical antipsychotic medications uses. This included areas such as; anxiety, ADHD, behavioral disturbances of dementia and severe geriatric agitation, MDD, eating disorders, insomnia, OCD, PTSD, personality disorders, substance abuse, and Tourette's syndrome. [2].

Therefore, can we really make assumptions?

Varian to leverage Linguamatics NLP text mining within the 360 Oncology care management platform

Cambridge, UK & Boston, USA – February 20th, 2017 – Clinical NLP provider Linguamatics, and Varian Medical Systems, today announced that Varian will utilize Linguamatics’ natural language processing (NLP) technology as part of the data analytics within Varian’s 360 Oncology™ care management platform.

Varian 360 Oncology care management is a software solution designed to meet the full spectrum of needs in oncology care management for hospitals and cancer centres at the oncology department level. It is capable of tracking physician and cancer specialist referrals, integrating evidence, outcomes data, guidelines and care pathways, coordinating data from multiple sites and settings including patients and external caregivers. Varian will utilize the Linguamatics Health platform, powered by Linguamatics I2E text mining technology, to extract unstructured concepts from within pathology reports and convert them to discrete data elements for analytics reporting within Varian 360 Oncology.

Clinical Trials text mining can speed key decisions, effective site selection and trial design 

Clinical trials form the cornerstone of evidence-based medicine, and are essential to establishing the safety and efficacy of new drugs. Each new drug, before being approved by regulatory agencies, must pass through a set of gates. At the very basic level these include phase 1 for first-in-human safety; phase 2 for efficacy and biological activity against the target; and phase 3 for safety, efficacy and effectiveness of the new therapeutic.

At each of these phases, careful planning is essential for a successful study. The clinical study protocol covers objective(s), design, methodology, statistical considerations and organization of a clinical trial, and ensures the safety of the trial subjects and integrity of the data collected.

Over recent years, clinical trial designs and procedures have become more diverse and more complex. The impact of precision medicine means trials have to be more carefully planned to ensure adequate statistical power for smaller patients groups, and adaptive, umbrella, basket and n-of-1 trials are now more frequent.

The regulatory requirements and growing complexity of clinical trials translates into more numerous and more complex eligibility criteria for study enrolment, increased site visits and required procedures, longer study duration, and more rigorous data collection requirements. From: PhRMA Biopharmaceutical Industry Profile 2016